In many medical applications, ultrasonic imaging has provided a low cost and effective method of diagnosing disease. B-scan images are two-dimensional maps of acoustic echoes from tissue components. These images have a textured or speckled appearance that is characteristic of the interaction between the fairly coherent pulse transmitted and the scattering sites in tissues. Texture is often viewed as image noise which hinders the detection and interpretation of such signals of soft tissue lesions. However, with appropriate statistical analysis, quantitative information specific to imaging performance and tissue characteristics can be extracted from the image texture
Detecting the presence of disease in tissue parenchyma on a quantitative, operator independent basis is the objective of tissue characterization methods. Toward this goal, many ultrasonic tissue characterization techniques have been proposed. The success of these methods depends, however, on how well measured acoustic properties or signal parameters correlate with disease states. The most widely studied characterization method is measurement of ultrasonic attenuation, which represents the total lineal loss of acoustic energy for ultrasound propagating through tissue. A number of spectral and time domain techniques have been proposed. Two attenuation techniques have been implemented in prototype commercial clinical B-scanning hardware.
Several patents, such as U.S. Pat. Nos. 4,475,397 and 4,515,163, have disclosed devices for determining the attenuation coefficient of tissue from zero crossings to frequency spectrum of reflected waves. Others like Miwa in U.S. Pat. No. 4,509,524 determine the attenuation coefficient of the tissue by comparing reflected waves of different, frequencies with a reference medium. The Flax et al. U.S. Pat. No. 4,475,396 discloses a time-based method of determining attenuation coefficient.
Stochastic methods for analyzing image texture have become a topic of increasing scientific interest because the results can be directly related to observable image characteristics and physical scattering properties. Several research groups have conducted off-line studies of the moments of first order statistics such as mean, variance and kurtosis as measures of tissue characterization. A common limitation of these studies is that the analysis is performed off-line with long turn-around times, diminishing effectiveness in any proposed clinical environment application.
Fellingham and Sommer (Ultrasonic Characterization of Tissue Structure in the In Vivo Liver and Spleen, IEEE Transactions on Sonics and Ultrasonics, Vol. SU-31, No. 4, July 1984) describe measurement of mean scatterer spacing as a tool for tissue characterization.
In all the above systems, there is either insufficient information for tissue characterization and discrimination, or there is not present a strong physical-statistical basis for the analysis of tissue images, specifically for discrimination in low contrast media.
Thus, in spite of the great need which has existed for many years, and the very great activity among researchers and practical workers in the art, there has not previously been provided a satisfactory system for rapidly detecting on-line the presence of disease in tissue parenchyma on a quantitative, operator independent basis, using ultrasonic imaging.